
HED[1]}:
Retaining the encoder part of SegNet, the holistically nested edge detection (HED) merges feature maps from five different levels in the encoder. The last feature map is used for computation of the loss function.

RCF[2]}:
Another edge detection technique for richer convolutional features (RCF) delivers a merged output from each convolutional layer in the encoder block while HED just outputs the final layer.

SegNet[3]}: This framework represents a standard end-to-end model with an auto-encoder.

DeepCrack[4]}: An end-to-end hierarchical network for crack extraction using the typical architecture of SegNet with symmetrical concatenation in the side branch.

FPHBN[5]}: The feature pyramid and hierarchical boosting network (FPHBN) is a recently proposed framework for crack detection and constructed on the main structure of HED.

FCN[6]}:
A simplified hourglass shape network for crack detection with only 6 blocks.

U-Net[7]}: An U-shape auto-encoder network with scale-invariant merging between outputs from the encoder and the decoder.

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